Abstract
Biological 3D object recognition is restricted to the sensing of 2D projections, or images, and is further constrained by the lack of transparency. The most common assumption then is that image data are referenced to mental object representations. Such representations, or object models, must be contrasted with object recognition in so far as the latter involves the understanding of image data. This distinction is central to recognition-by-components (RBC; Biederman 1987), a theory of human image understanding based on the assumption that input images are parsed into regions that display nonaccidental properties of edges. These properties provide critical constraints on the identity of 3D primitives (“geons”) the images come from, e.g., cylinders, blocks, wedges, and cones, and are (relatively) invariant with viewpoint and image degradation.
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Gschwind, M., Brettel, H., Rentschler, I. (2007). Prior Knowledge and Learning in 3D Object Recognition. In: Osaka, N., Rentschler, I., Biederman, I. (eds) Object Recognition, Attention, and Action. Springer, Tokyo. https://doi.org/10.1007/978-4-431-73019-4_8
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DOI: https://doi.org/10.1007/978-4-431-73019-4_8
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